utilisation du cloud dans les systèmes intelligent
DESCRIPTION
Les "systèmes intelligents" constituent la nouvelle génération de systèmes embarqués, qui, en s'appuyant sur les caractéristiques de robustesse et de déterminisme de leurs aînés, se connectent au cloud afin d'enrichir l'expérience utilisateur, qu'il s'agisse d'entreprises (collectant des données ou surveillant des systèmes par exemple), de particuliers (à la maison ou dans un contexte médical, ou bien dans la voiture) ou bien d'autres machines (dans le cas de systèmes automatisés à grande échelle). Le cloud et particulièrement Windows Azure fourni les vecteurs de communication et les moyens de stocker massivement des données et de les traiter, déchargeant ainsi les installations locales et donc rendant le déploiement de ses systèmes plus simple. Cette session, riche en exemples concrets, présentera la stratégie qui est celle de Microsoft autour du futur des systèmes embarqués, et leur connexion au cloud, ainsi que les technologies et les partenariats mis en oeuvre pour accélérer ces déploiements de systèmes intelligents. avec un exemple qui parlera à tous: le futur de la voiture, avec Windows Embedded Automotive!TRANSCRIPT
palais des congrès Paris
7, 8 et 9 février 2012
Mardi 7 févrierCharlie GrabiaudPartner Technology ManagerWindows Embedded, Microsoft
Utilisation du Cloud dans les Systèmes Intelligents
Analytics from Edge to Cloud
Windows Embedded+Azure scenarios
Evolution of Embedded Devices
Agenda
Market opportunity
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 20150.0
2.0
4.0
6.0
8.0
10.0
Billions of systems
Traditional Embedded Intelligent Systems
$520 BillionToday
$1.2 TrillionBy 2015
WW market
Today800 Million
unitsper year
20152.3 Billion units
per year
IDC, 2011
Intelligent Systems
Identity
Security
Connectivity
Manageability
User experience
Analytics
Stage B:Connected System
Stage C:Managed System
Stage D:Analytical System
Stage A:Discrete Technology
Solutions
System for a specific business
purpose
Limited automatic data-flow between
devices and back end
Data and information is shared between two or more systems
Connected devices allow data to be automatically updated in back end systems
Two-way connectivity allows for remote management of devices
System capable of analytics and BI
Stages of Intelligent Systems
Microsoft Confidential
EmbeddedDevice
1
Connectivity2EnterpriseBack-end
3
45
Data is the New Currency
Device Systems Analytics
Heartbeat (On, Off)Performance
EfficiencyProductivityTelemetry
Health and Performance
Data
CRM (Customer)ERP (Inventory, Employee)Market IntelligenceFraud/Theft Detection
System Related Data
TransactionsLogisticsRecordsEvents
System Interaction
Data
WeatherTrafficGPSMaps
Contextual Data
Too slowResults have outlived their value
Too vagueNo context to the data
Too muchVast amount of data, little information
Too littleMissing the right data
Too costlyHigh integration costs,
Complex toolsets
Today’s Data Challenges
$
Data Generation vs Capacity
2010 2015 2020
Data generation
Data hardwarecapabilities
Bandwidth / Servercapacity
Processing all the data centrally in premises becomeseither a bottleneck or too costly:• Must bring some of the processing
closer to the data source• Must use public cloud scaling
Benefits of Public Cloud Computing
Data & services accessible from anywhereOffice, Home and on the road
Almost unlimited resourcesInternet-scale computing and services platform
Very high availabilityAutomatic data redundancy and distributionRobustness of Microsoft's datacenters and Windows Azure
Cost optimizationNo huge CAPEX before development can startPay Per Use Model Good Windows Azure Applications are scalable by definition
Microsoft Intelligent Systems support
Business Intelligence
Network
Devices
Windows Embedded CompactWindows Embedded StandardWindows Embedded Enterprise, …
Windows AzureWindows Embedded ServerWindows Embedded Storage Server, …
Microsoft SQLMicrosoft DynamicsMicrosoft Sharepoint, ...
Windows Embedded+Azure Scenarios• Industrial Automation• Automotive• Public Services• Energy• Medical
Industrial cloud services
Storage of auditable dataSmall and mid size companies without own DCLong-term backup and availability
Device Monitoring and remote maintenanceMachines and equipment in remote locations
Web based engineeringMore computing power for compilingTeam engineering across multiple locations
Siemens/Intel/Microsoft POC
Cooperation ofSiemensIntelMicrosoft
Data in SQL Azure
Services onWindows Azure
Siemens DevicesWindows EmbeddedIntel CPUs
Today
60 Million Cars/Light Trucks
<10%
100%
Market Share
$3000
Standard (Free)
End User Cost
2015
80 Million Cars/Light Trucks
100%
Market Share
Standard (Free)
End User Cost
Global Infotainment Trends
Device Types
Video/3D Nav/Online Services
Color Screen/Speech UI/Navigation
USB/BT Telephone/Media
Radio/CD/MP3 Playback
20%
50%
70%
$2000
< $1000
Standard or $250
30%
40%
$1000
< $500
20%
50%
70%
$2000
< $1000
Standard or $250
30%
40%
$1000
< $500
The Automotive Design Lifecycle
Today Long Lead Times and Fixed Functionality
Standard Practice
Emerging Faster Development Cycles, Annual Releases, Continuous New Functionality
New Systems Model
Supplier / Platform Selection & Dev. Process
Production
7 years
Maintenance/Support
10-15 years…
Research
3-5 yr
Evaluation
1-2 yr
Development
2+ years
SOP
Platform Development Process
Production and Annual Releases
7 years
Maintenance/Support
10-15 years…
Research
3-4 Mo
Evaluation
3-4 Mo
Development
9 Mo
V2 V3 V4 V5 V6 V7SOP
Daimler Project: eMobility
• Enables drivers remote access to vehicle information
• Monitor charging state and possible range
• Combine car data with other information
• Access data at any time from every device
eMobility: Visualize data
• Use Bing routing service to calculate possible range
• Combine additional information and charging spot location for exact calculation
• Increase confidence in vehicle possibilities
Giletta, Italy : Intelligent Salt Spreading
Objectives- Spreading performance and cost- Safety on the road- Environmental impact- Better alignment to weather conditions.
Situation• F
leet of Trucks spreading salt on the road when snowing
• Truck drivers control the spreading manually, using predefined route
Challenges• S
alt is expensive
• Unnecessary pollution created by trucks
• Slow in some areas – no dynamic system to chose spreading location
• System not effective
Maps view
Spreading Parameters
SnowploughControls
Dedicated CAN Bus
iMx27 withWinCE6 R3
Intelligent System solution•On-board navigationand control system
•Back-end systemaggregating andcomputing data
Technology Enabler
s• W
indows Embedded To power the on-board controller (ARM, Real Time & connectivity)
• Windows Azure
Cloud-based application to analyze data and enable decision
New
Usage Scenari
o• P
rovide accurate directions to the driver
• New Data collection of highway infrastructures and services, weather, truck location data and traffic data
Creation of Additional business
value• Hundreds of
tons of salt saved
• Improved security on the road
• Reduced maintenance costs
• Reduced environmental impact
• Planned extension to other services (transport or recycling)
Home Energy Gateway Architecture
Internet Portal:• Secure Access• Customizable Content• Services Catalog• Services & Product Search• Client Data
Multi Channel and Multi DeviceMobile, PC, TV, Other
Home
PLC
Integration & Analytics:• Base Services (ex. Authentication)• Backoffice system integration• Services Directory (reusability)• Business Analytics
Home Energy Gateway
Back-End Systems
Home Energy Gateway Architecture
Smart Meters
Towards Intelligent Medical Systems Health drivers
–Aging population– Increasing costs–Prevalence of chronic disease–Consumer expectations of service quality and life style
continuity–Significant and accelerating staffing shortages
Health Intelligent Systems– Intelligent/connected medical devices (glucometers, blood
pressure monitors)–Electronic medical record (EMR)/personal health record
(PHR) systems–Care management systems (enables remote care by
clinicians)–Telemedicine and remote patient monitoring–Telepresence/video conferencing–Patient portals
Medical Proof Of Concept
HealthVault in Medical POC
Analytics from Edge to Cloud
Understanding Streaming Data (1)
Question: “how many red cars are in the parking lot”.
Answering with a relational database:• Walk out to the parking lot.• Count vehicles that are
- Red- Cars
SELECT COUNT(*) FROM ParkingLotWHERE type = ‘AUTO’AND color = ‘RED’
Understanding Streaming Data (2)
Answering with a relational database:• Pull over and park all vehicles in a lot,
keeping them there until the end of the hour.• At the end of the hour, count vehicles that
are in the lot.• Then deliver the answer
Doesn’t seem like a great solution…
What about: “How many red cars took the I-80 interchange to San Francisco in the last hour”?
Understanding Streaming Data (3)
Different kinds of questions require different ways of answering them.
This is the streaming data paradigm in a nutshell – ask questions about data in flight.
The last questions we looked at are best answered with a stream data processing engine, or complex event processing engine.
How would a streaming engine do the processing for this scenario?• Stand by the freeway, count red cars as they pass by.
• Keep updating the answer internally, keep delivering the answer as needed by the consumers.
Event-Driven Applications
Analytical results need to reflect important changes in business reality immediately and enable responses to them with minimal latency
Query Paradigm
Latency
Data Rate
Query Semantics
Database-driven Applications
Ad-hoc queries or requests
Seconds, hours, days
Hundreds of events/sec
Declarative relational analytics
Event-driven Applications
Continuous standing queries
Milliseconds or less
Tens of thousands of events/sec or more
Declarative relational and temporal analytics
request
response
Event output stream
input stream
Example: Microsoft Campus Shuttle Bus Tracking
• Plot current position for Redmond campus shuttles
• Track specific shuttles• Identify when shuttles
approach specific destinations
• Proximity queries with SQL Spatial Libraries
Scenarios for Event-Driven Applications
Relational Database Applications
Financial trading Applications
Latency
0 10 100 1000 10000 100000 ~1million
Months
Days
Hours
Minutes
Seconds
100 ms
< 1ms
Operational Analytics Applications, e.g., Logistics, etc.
Manufacturing ApplicationsMonitoring Applications
Data Warehousing Applications
Web Analytics Applications
Aggregate Data Rate (Events/sec.)
StreamInsight™
MicrosoftStreamInsight™
Rich Analytics
Intelligent Processing
Unified Experience
Optimize data traffic
• Continuous processing of event streams from multiple sources
• Based on rich declarative query language• Optimized for analytics over time-series data
• Express and detect complex pattern and device profiles
• Push richer analytics down to the device (pattern redeployment)
• Provide uniform semantics & development experience from server to the edge
• Seamlessly transition between historical and real-time data
• Send only relevant information from device• Eliminate bottleneck at the mid-tier
StreamInsight™Application Development
StreamInsight™ Platform
Event targets
`
Event stores & Databases
Pagers &Monitoring devices
KPI Dashboards, SharePoint UI
Trading stations
Event sources
Devices, Sensors
Web servers
Event stores & Databases
Stock ticker, news feeds
Standing Queries
Query Logic
Query Logic
Query Logic
InputAdapters
OutputAdaptersStreamInsight™ Engine
StreamInsight™ Application at Runtime
Analytics Platform
Hosted in the cloud/on-premise
• Gather insight from large collections of assets
• Mine historical data to create/validate new models
Embedded in the asset
• Creates adaptable, network friendly, remotely manageable assets
Integrated with .NET
• Extensible to incorporate domain specific analytic needs
• Rich development tools to reduce total cost of ownership
Global, cross-asset analytics for aggregation and correlation of in-flight events; analytics on historical data
SI
Per-asset analytics for lightweight processing and filtering, computed close to the asset
SI
Assets
Cross-asset Analytics & Mining
SI SI
Robots
SI
Sensors
SI
Process & Control
SI Auto
SI
OEM Engr.
Connected Car Scenario
Analytics Computation
AssetAnalytic
High Customer SatisfactionCar Operation
New models, updates, etc. for deployment
Recommendations(Route, recharging station, business location, etc.)
Servicing/ Diagnostics(Service recommendation, Updates, etc.)
Significant Operational Data (Battery level, engine status, speed etc.)
Location Data (GPS coordinates)
Contextual Data(Destination, address, etc.)
Exploratory analysis of historical data across cars to
identify problems or enhance driver experience
World of Windows Embedded
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